Reconstruction of original touch image from differential touch image

ABSTRACT

Reconstruction of an original touch image from a differential touch image is disclosed. Reconstruction can include aligning columns of the differential touch image relative to each other and aligning the image to a baseline DC value. The column and baseline alignment can be based on the differential image data indicative of no touch or hover, because such data can more clearly show the amount of alignment needed to reconstruct the original image. The reconstruction can be performed using the differential image data alone. The reconstruction can also be performed using the differential image data and common mode data indicative of the missing image column average.

FIELD

This relates generally to a differential touch image and morespecifically to reconstructing an original touch image from adifferential touch image.

BACKGROUND

FIG. 1 illustrates an exemplary touch image captured by a touchsensitive device, such as a touch panel. In the example of FIG. 1, touchimage 100 can include rows 101 and columns 102 of image data (depictedby small circles), where the image data values can indicate an area 110of the device at which an object touched or hovered over the panel(depicted by a broken circle), and the remaining untouched area 120.

Two types of touch images that can be captured include an original touchimage and a differential touch image, depending on the scanconfiguration of the device. FIG. 2 illustrates an exemplary column 102from the touch image 100 of FIG. 1 showing what the image data 225 canlook like in an original touch image and what the image data 235 canlook like in a differential touch image. For explanatory purposes, theimage data is shown in FIG. 2 as continuous, although it should beunderstood that the image data can also be discrete as illustrated inFIG. 1. In the original touch image, the image data indicative of anuntouched area 225 a can have a value of zero and the image dataindicative of a touched area 225 b can have values greater than zero,depending on the proximity of the touching or hovering object. Theoriginal touch image can include actual magnitudes of the image values.In the differential touch image, each column of image data can bebalanced to have an average value of zero, i.e., having the DCinformation removed. As such, the image data indicative of an untouchedarea 235 a can have a negative value and the image data indicative of atouched area 235 b can have negative and positive values depending onthe proximity of the touching or hovering object. As such, the column'snegative and positive values can balance out to zero. The differentialtouch image can include differential (or relative) magnitudes of theimage values.

A touch sensitive device capable of generating a differential touchimage can have some advantages. For example, some device hardware usedto generate an original touch image can be eliminated, thereby freeingup space to expand the device's viewing area. Also, image effects, suchas thermal drift, can have little or no effect on a differential image.However, because many touch sensitive devices require the actual imagevalue magnitudes (such as provided by an original touch image) toperform various functions, a differential touch image can have limiteduse.

SUMMARY

This relates to reconstructing an original touch image from adifferential touch image in a touch sensitive device. A reconstructionmethod can include aligning columns of the differential touch imagerelative to each other based on image data indicative of no touch orhover (“untouched data”). Untouched data can more clearly show how muchcolumn alignment is needed compared to image data indicative of a touchor hover (“touched data”). The method can further include aligning theimage to a baseline value based on the untouched data in order torestore the previously removed DC information to the differential image.The untouched data can more clearly show how much baseline alignment isneeded compared to the touched data. In one example, the reconstructioncan be performed using the differential image data alone. In anotherexample, the reconstruction can be performed using the differentialimage data and common mode data indicative of the missing image columnaverage. The ability to use the differential touch image to reconstructthe original touch image can advantageously provide the benefits of thedifferential image while performing device functions with thereconstructed original image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary touch image according to variousembodiments.

FIG. 2 illustrates an exemplary column of an original touch image and adifferential touch image according to various embodiments.

FIG. 3 illustrates an exemplary method for reconstructing an originaltouch image from a differential touch image according to variousembodiments.

FIG. 4 illustrates an exemplary row of a differential touch image beforeand after column alignment according to various embodiments.

FIG. 5 illustrates an exemplary row of a differential touch image beforeand after baseline alignment according to various embodiments.

FIG. 6 illustrates an exemplary method for reconstructing an originaltouch image from a differential touch image based on image dataaccording to various embodiments.

FIGS. 7A-7B illustrate an exemplary method for detecting baselineinversion in a differential touch image according to variousembodiments.

FIGS. 8A-8B illustrate an exemplary method for reconstructing anoriginal touch image from a differential touch image based on image dataand common mode data according to various embodiments.

FIGS. 9A-9B illustrate another exemplary method for reconstructing anoriginal touch image from a differential touch image based on image dataand common mode data according to various embodiments.

FIG. 10 illustrates another exemplary method for reconstructing anoriginal touch image from a differential touch image based on image dataaccording to various embodiments.

FIG. 11 illustrates an exemplary computing system that can performoriginal touch image reconstruction according to various embodiments.

FIG. 12 illustrates an exemplary mobile telephone that can performoriginal touch image reconstruction according to various embodiments.

FIG. 13 illustrates an exemplary digital media player that can performoriginal touch image reconstruction according to various embodiments.

FIG. 14 illustrates an exemplary portable computer that can performoriginal touch image reconstruction according to various embodiments.

DETAILED DESCRIPTION

In the following description of example embodiments, reference is madeto the accompanying drawings in which it is shown by way of illustrationspecific embodiments that can be practiced. It is to be understood thatother embodiments can be used and structural changes can be made withoutdeparting from the scope of the various embodiments.

This relates to reconstructing an original touch image from adifferential touch image in a touch sensitive device. A reconstructionmethod can include aligning columns of the differential touch imagerelative to each other based on image data indicative of no touch orhover (“untouched data”). Untouched data can more clearly show how muchcolumn alignment is needed compared to image data indicative of a touchor hover (“touched data”). The method can further include aligning theimage to a baseline value, representative of the previously removed DCinformation, based on the untouched data. The untouched data can moreclearly show how much baseline alignment is needed compared to thetouched data. In some embodiments, the reconstruction can be performedusing the differential image data alone. In other embodiments, thereconstruction can be performed using the differential image data andcommon mode data (i.e., output from a touch device when there is noproximate object). The ability to use the differential touch image toreconstruct the original image can advantageously provide the benefitsof the differential image while also performing device functions withthe reconstructed original image.

In some embodiments, the differential touch image can be a fullydifferential touch image. Although reconstruction from offsets based onDC balancing is described herein, it should be understood thatreconstruction based on any image offsets can be performed according tovarious embodiments.

FIG. 3 illustrates an exemplary method for reconstructing an originaltouch image from a differential touch image. In the example of FIG. 3, adifferential touch image can be captured during a differential scan of atouch panel (310). In some embodiments, the differential scan canperform a differential drive operation as follows. All the rows of thetouch panel or a subset of rows can be driven during each step of thescan. For the driven rows, some can be driven with stimulation signalshaving positive phase and others with stimulation signals havingnegative phase. The static (non-signal related) capacitance that isapproximately uniform across the touch panel may not appear in theoutput, because of the combination of positive and negative phases inthe drive signals. After several scan steps in which differentcombinations of rows are driven with various stimulation signal phasesand patterns, the difference between the touch data values can becalculated, thereby forming the differential touch image.

In some embodiments, the differential scan can perform a differentialsense operation as follows. The rows of the touch panel can be drivenone at a time, for example, with a stimulation signal. The sensingamplifiers on the columns of the touch panel can be differential sensingamplifiers, where each measures the difference between two columns,between a column and the average of all the other columns, or betweenone subset of columns and another subset of columns. The static(non-signal related) capacitance that is approximately uniform acrossthe touch panel may not appear in the output, because the sensingamplifiers measure only differences. After the scan, the differencebetween the touch image values on each row may be calculated, therebyforming the differential touch image.

In some embodiments, the differential scan can combine the differentialdrive and differential sense operations to capture the differentialtouch image.

As described previously in FIG. 2, each column of image data can have anaverage value of zero, with untouched data being negative and toucheddata being negative and positive depending on the object's proximity,thereby rendering the sum of the column zero. Due to differing touchlevels (as well as noise and other effects), each column of image datacan have different average values. These differences can be mostapparent in the untouched data, where negative values for all thecolumns can tend to differ. To correct these differences, the columnscan be aligned relative to each other using the untouched data as aguide (320). Methods for performing the column alignment will bedescribed in detail below.

FIG. 4 illustrates an example of column alignment in the touch image ofFIG. 1. In the example of FIG. 4, an exemplary row (labeled “A-A” inFIG. 1) of untouched data is shown for a differential touch image. Priorto column alignment, the row shows the erroneous differences between theuntouched data values 435 (depicted by small circles) across all thecolumns 102 in the image. After column alignment, the row shows theuntouched data values 445 across all the columns 102 having the samenegative value N. The data values for the touched data in each column(not shown) can be adjusted according to the adjustments made to theircorresponding untouched data.

Referring again to FIG. 3, after the columns have been aligned, theimage data still lacks the DC information. This can be most apparent inthe column-aligned untouched data, where the data value including the DCinformation should be around zero, not negative. To restore the DCinformation, the image can be aligned relative to a baseline value B,representative of the DC information, using the column-aligned untoucheddata as a guide (330). Methods for performing the baseline alignmentwill be described in detail below.

FIG. 5 illustrates an example of baseline alignment in thecolumn-aligned row of FIG. 4. In the example of FIG. 5, an exemplary rowof column-aligned untouched data from FIG. 4 is shown. Prior to baselinealignment, the row shows untouched data 545 (depicted by small circles)with a negative data value N. After baseline alignment, the row showsthe image data 555 having a data value corresponding to the baselinevalue. In most embodiments, the baseline value is zero. The data valuesfor the touched data in each column (not shown) can be adjustedaccording to the adjustments made to their corresponding untouched data,resulting in all positive values for the touched data.

Reconstructing an original touch image from a differential touch imagecan be formulated in mathematical terms. Mathematically, aligning thecolumns of a differential touch image with each other can be formulatedas selecting a set of relative column offsets which minimize the rowedge energy, where each edge has a weight vector associated with it. Theweights can be chosen so as to weight the untouched data heavily. Thisis because the required column alignments cane be more apparent in theuntouched data, as described previously. Mathematically, aligning theimage to a baseline value can be formulated as finding an overall offsetof the image which minimizes the total image energy using the edgeweights. FIGS. 6 through 10 illustrate exemplary methods for performingthe column and baseline alignments according to these mathematicalformulations as will be described in detail below.

FIG. 6 illustrates an exemplary method for reconstructing an originaltouch image from a differential touch image using the differential imagedata. In the example of FIG. 6, a differential touch image can becaptured during a differential scan of a touch panel (610). Consider a3×3 differential touch image C as follows.

$\begin{matrix}{{C = \begin{matrix}C_{11} & C_{12} & C_{13} \\C_{21} & C_{22} & C_{23} \\C_{31} & C_{32} & C_{33}\end{matrix}},} & (1)\end{matrix}$where C_(ij)=image data value, i=row, and j=column. For example, for 3×3image C, C₂₃=the image data value for row 2 and column 3.

Row edge energy can be calculated from the image C and formed in amatrix E as follows (615).

$\begin{matrix}{{E = \begin{matrix}E_{11} & E_{12} & E_{13} \\E_{21} & E_{22} & E_{23} \\E_{31} & E_{32} & E_{33}\end{matrix}},} & (2)\end{matrix}$where E_(ij)=row edge energy value defined as (C_(ij)−C_(i,j+1)). Thefirst and last columns of the image C can be mathematically connected totreat the image as a cylinder for calculation purposes. For example, for3×3 matrix E,E ₁₁=(C ₁₁ −C ₁₂)  (3)E ₁₂=(C ₁₂ −C ₁₃)  (4)E ₁₃(C ₁₃ −C ₁₁).  (5)

Weights associated with each edge can be calculated and formed in amatrix W as follows (620).

$\begin{matrix}{{W = \begin{matrix}W_{11} & W_{12} & W_{13} \\W_{21} & W_{22} & W_{23} \\W_{31} & W_{32} & W_{33}\end{matrix}},} & (6)\end{matrix}$where W_(ij)=edge weight for E_(ij). For example, W₂₃=edge weight forE₂₃.

The success of the column and baseline alignment can depend to someextent on choosing the appropriate edge weights. Ideally, the weightscan be chosen based on the probability of the image data making up thatedge being untouched data. A variety of weight calculations can beselected from. In one embodiment,W _(ij)=[100−(C _(ij)−MIN_(j))],  (7)where MIN_(j)=minimum data value of image column j.

In another embodiment,W _(ij)=[100−abs(C _(ij)−MED_(j))]  (8)where MED_(j)=median of the negative data values of image column j.Here, only the negative data values are considered because the median ofthe negative values can be a more robust estimator of the currentbaseline value of the differential touch image. The estimated baselinevalue of the differential image can then be indicative of the baselinealignment to be done to reconstruct the original image.

In still another embodiment,W _(ij)=max[(W _(max)−abs(C _(ij)−MED_(j))),W _(min)],  (9)where W_(min), W_(max)=minimum and maximum edge weights, respectively.These weights can be set according to design or operational parametersof the device. For example, in some embodiments, W_(min)=1/5,W_(max)=75.

In other embodiments, mode can be used instead of median,W _(ij)=[100−abs(C _(ij)−MOD_(j))]  (10)W _(ij)=max[(W _(max)−abs(C _(ij)−MOD_(j))],  (11)where MOD_(j)=modal of the negative data value of image column j.

In other embodiments, a mean or a weighted mean of the negative datavalues of each image column j can be used rather than minimum, median,or mode to calculate weights, similar to Equations (7)-(11). Any othersuitable image data parameters can be used according to variousembodiments.

In other embodiments, any of the above weighting schemes can be used foran initial weighting. This initial weighting can then be modified toaccount for the presence of other non-touch/non-baseline effects, suchas the negative pixel effect. One such weighting can includingcalculating the affected image data N_(ij) as follows. N_(ij)=1, ifthere exists at least one image data value in image row i and imagecolumn j that is greater than zero. N_(ij)=0, otherwise. Accordingly,W _(ij,a) =W _(ij) −W _(ij) ·N _(ij) ·k  (12)where k=an attenuation factor. In some embodiments, k=0.5. Here, weightsW_(ij) for negative pixel influenced image data can be attenuated toproduce attenuated weights W_(ij,a).

In the above embodiments, the edge weights can be the minimum of twoweights. In other embodiments, the maximum of the two weights and theirarithmetic and geometric mean can be considered.

Also, in the above embodiments, the edge weights can be determined forthe general case. In other embodiments, the edge weights can bedetermined based on variance in the untouched data of each column. To dothis, the mode of each column can be calculated and the number ofoccurrences of the mode determined. If the mode is sufficiently strong,i.e., the number of occurrences high, a smaller maximum edge weight andthe modal formulation (Equations (10), (11)) can be used. As the modeweakens, i.e., the number of occurrences decreases, the variance canincrease. If the mode is sufficiently weak, i.e., the number ofoccurrences is low enough, a larger edge weight can be used with themedian formulation (Equations (8), (9)). This can allow the weightcalculation to better reflect the available data.

Relative column offsets R, i.e., the amount of column alignment to bedone for each column relative to an adjacent column, can be calculatedas follows (625). First, the following mean square error for R_(j) canbe minimized,Σ_(ij) W _(ij)(E _(ij) +R _(j))²,  (13)where R_(j)=relative offset for image column j.

The relationship between the relative column offsets R and absolutecolumn offsets A, i.e., the absolute amount of column alignment to bedone for each column, as illustrated in Equation (17), can be formulatedas follows.R _(j) =A _(j) −A _(j-1),  (14)where A_(j)=absolute offset for image column j. The first and lastcolumns of the differential image C can be mathematically connected totreat the image as a cylinder for calculation purposes. For example, for3×3 image C, R_(i)=A₁−A₃.

The partial derivatives of R_(j) can be set to zero,∂R _(j)=2Σ_(i) W _(ij)(E _(ij) +R _(j))=0  (15)

R_(j) can be found,

$\begin{matrix}{R_{j} = {\frac{\Sigma_{i}W_{ij}E_{ij}}{\Sigma_{i}W_{ij}}.}} & (16)\end{matrix}$

Absolute column offsets A can be calculated based on the relativeoffsets R as follows (630).

$\begin{matrix}{{{M_{ij}A_{j}} = R_{j}},} & \square \\{M = {\begin{matrix}1 & {- 1} & 0 \\0 & 1 & {- 1} \\{- 1} & 0 & 1\end{matrix}.}} & (18)\end{matrix}$

Equation (18) can have multiple solutions, such that there is no trueinverse of matrix M. Accordingly, a pseudo-inverse matrix P can begenerated and applied as follows to get the absolute column offsetsA_(j) for each column.A _(j) =P _(ij) R _(j),  (19)where P_(ij)=pseudo-inverse value of corresponding M_(ij) value.

Next, a global offset A_(g) can be calculated for the baseline alignment(635). The global offset A_(g) can be calculated based on image data atthe borders of the image, because the border data can be assumed to beuntouched data, as follows.

$\begin{matrix}{A_{g} = {\frac{\Sigma_{border}{W_{ij}\left( {C_{ij} + A_{j}} \right)}}{\Sigma_{border}W_{ij}}.}} & (20)\end{matrix}$The global offset A_(g) can also be formulated as a sum over the entiretouch image, not just the border data.

The absolute column offsets A_(j) can be adjusted based on the globaloffset as follows (640).A _(j,f) =A _(j) +A _(g),  (21)where A_(j,f)=adjusted absolute offset for image column j.

The adjusted absolute offsets A_(j,f) can be applied to the differentialtouch image C as follows (645).I _(ij) =C _(ij) +A _(j,f),  (22)where I_(ij)=reconstructed original touch image data from differentialtouch image data C_(ij).

Applying the offsets A_(j,f) can align the columns of the image Crelative to each other and the image to the baseline, therebyreconstructing an original touch image I from the differential touchimage C.

Although the example refers to 3×3 matrices, it is to be understood thatthe method applies to any suitable matrix sizes according to variousembodiments.

An inverted baseline can occur when a touching or hovering object ispresent at the time that a baseline value for the touch panel isestablished. This can be problematic in a differential touch imagebecause the inherent DC balancing of the image data can make theinverted case look like an intended actual touch or hover and becausethe current baseline value is estimated based on the median of thenegative touch values (as described previously). For example, adifferential touch image with a single inverted touch or hover canappear as two touching or hovering objects in the reconstructed originaltouch image.

FIGS. 7A-7B illustrate an exemplary method for detecting theseinversions in the method of FIG. 6. In the example of FIGS. 7A-7B,common mode data can be captured at the touch panel (705). Common modedata can refer to data output from a touch panel when the rows in thepanel are driven simultaneously, for example. In other words, commonmode data can be the average values for the image columns. Note that thecommon mode data need not be accurate, i.e., need not have low noiseand/or variance. Common mode data can be captured during a separate scanperiod than the differential touch image scan period. In someembodiments, multiple scans can be performed to capture common mode datafor each column and the captured data averaged to provide an averagecommon mode value for each column.

The method of FIGS. 7A-7B can then proceed in a similar manner as themethod of FIG. 6 (blocks 610-630) to capture a differential touch imageC (710), calculate row edge energy E (715), calculate edge weights W(720), calculate relative column offsets R (725), and calculate absolutecolumn offsets A (730).

After calculating the offsets A (730), common mode parameters can becalculated based on the captured common mode data as follows (735). Thecommon mode mean CMM is

$\begin{matrix}{{{CMM} = \frac{\Sigma_{j}{CM}_{j}}{n_{cols}}},} & (23)\end{matrix}$where CM_(j)=common mode data, and n_(cols)=number of columns j in theimage. The mean-subtracted common mode MCM isMCM_(j)=CM_(j)−CMM.  (23)

The mean Ā of the absolute column offsets A can be calculated as follows(740). The offsets A can effectively be an estimate of the common modedata.

$\begin{matrix}{\overset{\_}{A} = {\frac{\Sigma_{j}A_{j}}{n_{cols}}.}} & (25)\end{matrix}$

A first inversion detection test can be performed, in which a comparisoncan be made between the offset mean and the common mode mean (745). Ifthe common mode mean CMM is substantially smaller than the offset meanĀ, a large-scale inversion is likely present. A panel scan can beperformed to capture another baseline (785) and the reconstruction canbe stopped for this image. If a large-scale inversion is not detected, asecond inversion detection test can be performed to look for a smallerscale inversion as described below.

First, the mean-subtracted column offsets for each column Â_(j) can becalculated as follows (750).Â _(j) =A _(j) −Â.  (26)

The second inversion detection test can compare the mean-subtractedcolumn offsets Â_(j) to the mean-subtracted common mode values MCM_(j)for each column (755). The column with the largest absolute valuebetween the two can be selected (760). For that column, if the signeddifference is substantially negative, an inversion is likely present. Apanel scan can be performed to capture another baseline (785) and thereconstruction can be stopped for this image. If a smaller scaleinversion is not detected, the image data can be deemed inversion-free.

The method can then perform in a similar manner as FIG. 6 (blocks635-645) for the inversion-free data to calculate a global offset A_(g)(770), adjust the absolute column offsets A with the global offset A_(g)(775), and apply the adjusted offset A_(j,f) to the differential touchimage C to reconstruct an original touch image I (780).

FIGS. 8A-8B illustrate an exemplary method for reconstructing anoriginal touch image from a differential touch image using differentialimage data and common mode data. The method of FIGS. 8A-8B is similar tothe method of FIG. 6 with the addition of the use of common mode data toestimate the baseline value of an image column (rather than using amedian of the negative touch values), thereby bypassing the inversiondetection method of FIGS. 7A-7B.

In the example of FIGS. 8A-8B, common mode data CM can be captured at atouch panel in a similar manner as described in FIGS. 7A-7B (802). Adifferential touch image C can be captured at the touch panel in asimilar manner as described in FIG. 6 (804). The common mode data CM canbe added to the captured image touch data C to create new image C′ asfollows (806).C′ _(ij) =C _(ij) +CM _(j).  (27)

The common mode data can include substantial noise in some instances,which can lead to significant variations between successive capturedimages. To reduce the noise effects, a histogram approach can be used toestimate a common mode mean offset CM_(o) as follows, under theassumption that most of the image data is untouched data, e.g., dataindicative of no touching or hovering object. First, histogram bins overthe range of data values in image C′ can be generated for a desired binsize (808). For example, in some embodiments, the bin size can be 16.For each data value in image C′ that falls into a given bin, that bin'scount can be incremented by 2.

Half of the bin size can be added to the image C′ to produce image C⁽²⁾(810). For example, in some embodiments, for a bin size of 16, a valueof 8 can be added to each data value C′_(ij) in the image C′. For eachnew data value (increased by half the bin size) in image C⁽²⁾ that fallsinto a given bin of the histogram, that bin's count can be incrementedby 1 (812). Half the bin size can then be subtracted from the image C′to produce image C⁽³⁾ (814). For example, in some embodiments, for a binsize of 16, a value of 8 can be subtracted from each data value C′_(ij)in the image C′. For each new data value (decreased by half the binsize) in image C⁽³⁾ that falls into a given bin of the histogram, thatbin's count can be incremented by 1 (816).

The histogram bins with the highest and second highest counts can beidentified (818). If the identified bins are adjacent (820), the commonmode mean offset CM_(o) can be the weighted average of the center valuesfor the two bins (822). If not, the common mode mean offset CM_(o) canbe the center value of the identified highest-count bin. (824). Theoffsets CM_(o) can be subtracted from the common mode values CM_(j) asfollows (826).BASE_(j)=CM_(j)−CM_(o).  (28)These resulting values BASEj can be estimates of the baseline values forthe image columns.

Next, the method of FIGS. 8A-8B can perform in a similar manner as themethod of FIG. 6 (blocks 615-645). Row edge energy can be calculated(828). Edge weights W can be calculated using BASE_(j) as follows.W _(ij)=[100−abs(C _(ij)−BASE_(j))].  (29)Equation (29) is similar to Equation (8) with BASE_(j) replacingMED_(j). Relative column offsets R can be calculated (832). Absolutecolumn offsets A can be calculated (834). A global offset A_(g) can becalculated (836). The absolute column offsets A can be adjusted with theglobal offset A_(g) (838). The adjusted offsets A_(j,f) can be appliedto the differential touch image C to reconstruct an original touch imageI (840).

FIGS. 9A-9B illustrate another exemplary method for reconstructing anoriginal touch image from a differential touch image using differentialimage data and common mode data. The method of FIGS. 9A-9B is similar tothe method of FIGS. 8A-8B with the differences noted below. In theexample of FIGS. 9A-9B (similar to FIGS. 8A-8B in blocks 802-826),common mode data can be captured (902), a differential touch image Ccaptured (904), the common mode data CM added to the captured image C(906), and the histogram approach applied (908-926).

Common mode errors, such as noise and other errors, can directly coupleinto the reconstructed original touch image. To minimize these effects,common mode data can be limited to being used to directly reconstructthe original touch image only when there is no other source ofinformation about the magnitudes of the differential image columns. Thesum of the edge weights along a given edge, e.g., W₁₁, W₂₁, W₃₁, etc.,formed by image columns 1 and 2, can be indicative of the amount ofmagnitude information available for performing column alignment. Thisknowledge can be integrated into the method as follows.

After using the histogram approach to calculate BASE_(j), estimates ofthe baseline values for the image columns (926), BASE_(j) can be addedto the differential touch image C to form image C′ as follows (928).C′ _(ij) =C _(ij)+BASE_(j).  (30)

The row edge energy can be calculated for image C′ (930) in a similarmanner as the method of FIGS. 8A-8B (block 828). Edge weights W can becalculated as follows.W _(ij)=max[(W _(max)−abs(C _(ij))),W _(min)].  (31)

If common mode data is trustworthy, a penalty term can be applied fordeviating from direct reconstruction, i.e., adding the common mode datato the differential image data to directly to reconstruct the originaltouch image, making the mean square error for R_(j),Σ_(ij) W _(ij)(E _(ij) +R _(j))² +γR _(j) ²,  (32)where γ=a penalty factor. In general, γ can be inversely proportional tocommon mode noise. For example, in some embodiment, γ=1.0. If adifferential image column includes mostly touched data, i.e., data thatindicates a touching or hovering object, the method can weigh moreheavily toward direct reconstruction because of the limited amount ofuntouched data, i.e., data that does not indicate a touching or hoveringobject, in that column to perform the column and baseline alignment.

To minimize the mean square error, the partial derivatives of R_(j) canbe set to zero,∂R _(j)=2Σ_(i) W _(ij)(E _(ij) +R _(j))+2γR _(j)=0.  (33)

R_(j) can be found,

$\begin{matrix}{R_{j} = {\frac{\Sigma_{i}W_{ij}E_{ij}}{{\Sigma_{i}W_{ij}} + \gamma}.}} & (34)\end{matrix}$

Next, the method of FIGS. 9A-9B can perform in a similar manner as themethod of FIGS. 8A-8B (blocks 834-840) to calculate absolute columnoffsets A (936), calculate a global offset A_(g) (938), adjust theabsolute column offsets A with the global offset A_(g) (940), and applythe adjusted offsets A_(j,f) to the differential touch image C toreconstruct an original touch image I (942).

FIG. 10 illustrates another exemplary method for reconstructing anoriginal touch image from a differential touch image using thedifferential image data. In the example of FIG. 10, a differential touchimage C can be captured (1005). In some embodiments, a minimum datavalue MIN_(j) for each image column j can be calculated (1010). Theminimum data value can be added to the differential image data, therebyreconstructing the original touch image I from the differential touchimage C as follows (1015).I _(ij) =C _(ij)−MIN_(j).  (35)

In some embodiments, a median MED_(j) of the negative data values ofimage column j can be calculated rather than MIN_(j) (1010). The mediancan be added to the differential image data, thereby reconstructing theoriginal touch image I from the differential touch image C as follows(1015).I _(ij) =C _(ij)−MED_(j).  (36)

In some embodiments, a mode MOD_(j) of the negative data values of imagecolumn j can be calculated rather than MIN_(j) or MED_(j) (1010). Themode can be added to the differential image data, thereby reconstructingthe original touch image I from the differential touch image C asfollows (1015).I _(ij) =C _(ij)−MOD_(j).  (37)

In some embodiments, a mean or a weighted mean of the negative datavalues of each image column j can be calculated rather than MIN_(j),MED_(j), or MOD_(j) and then added to the differential image data toreconstruct the original touch image, similar to Equations (35)-(37).Any other suitable image data parameters can be used according tovarious embodiments.

It is to be understood that reconstruction methods are not limited tothose of FIGS. 6 through 10, but can include other or additional actionscapable of reconstructing an original touch image from a differentialtouch image according to various embodiments. It is further to beunderstood that the methods are not limited to differential touchimages, but can be applied to any appropriate images in need ofreconstruction. Although reconstruction from offsets due to DC balancingis described, it is to be understood that the methods can be used tocorrect for any arbitrary set of column offsets.

FIG. 11 illustrates an exemplary computing system 1100 that canreconstruct an original touch image from a differential touch imageaccording to various embodiments. In the example of FIG. 11, computingsystem 1100 can include touch controller 1106. The touch controller 1106can be a single application specific integrated circuit (ASIC) that caninclude one or more processor subsystems 1102, which can include one ormore main processors, such as ARM968 processors or other processors withsimilar functionality and capabilities. However, in other embodiments,the processor functionality can be implemented instead by dedicatedlogic, such as a state machine. The processor subsystems 1102 can alsoinclude peripherals (not shown) such as random access memory (RAM) orother types of memory or storage, watchdog timers and the like. Thetouch controller 1106 can also include receive section 1107 forreceiving signals, such as touch signals 1103 of one or more sensechannels (not shown), other signals from other sensors such as sensor1111, etc. The touch controller 1106 can also include demodulationsection 1109 such as a multistage vector demodulation engine, panel scanlogic 1110, and transmit section 1114 for transmitting stimulationsignals 1116 to touch sensor panel 1124 to drive the panel. The panelscan logic 1110 can access RAM 1112, autonomously read data from thesense channels, and provide control for the sense channels. In addition,the panel scan logic 1110 can control the transmit section 1114 togenerate the stimulation signals 1116 at various frequencies and phasesthat can be selectively applied to rows of the touch sensor panel 1124.

The touch controller 1106 can also include charge pump 1115, which canbe used to generate the supply voltage for the transmit section 1114.The stimulation signals 1116 can have amplitudes higher than the maximumvoltage by cascading two charge store devices, e.g., capacitors,together to form the charge pump 1115. Therefore, the stimulus voltagecan be higher (e.g., 6V) than the voltage level a single capacitor canhandle (e.g., 3.6 V). Although FIG. 11 shows the charge pump 1115separate from the transmit section 1114, the charge pump can be part ofthe transmit section.

Touch sensor panel 1124 can include a capacitive sensing medium havingdrive lines and multiple strips of sense lines according to variousembodiments. The drive and sense line strips can be formed from atransparent conductive medium such as Indium Tin Oxide (ITO) or AntimonyTin Oxide (ATO), although other transparent and non-transparentmaterials such as copper can also be used. The drive lines and senseline strips can be formed on a single side of a substantiallytransparent substrate separated by a substantially transparentdielectric material, on opposite sides of the substrate, on two separatesubstrates separated by the dielectric material, etc.

Computing system 1100 can also include host processor 1128 for receivingoutputs from the processor subsystems 1102 and performing actions basedon the outputs that can include, but are not limited to, moving anobject such as a cursor or pointer, scrolling or panning, adjustingcontrol settings, opening a file or document, viewing a menu, making aselection, executing instructions, operating a peripheral device coupledto the host device, answering a telephone call, placing a telephonecall, terminating a telephone call, changing the volume or audiosettings, storing information related to telephone communications suchas addresses, frequently dialed numbers, received calls, missed calls,logging onto a computer or a computer network, permitting authorizedindividuals access to restricted areas of the computer or computernetwork, loading a user profile associated with a user's preferredarrangement of the computer desktop, permitting access to web content,launching a particular program, encrypting or decoding a message, and/orthe like. The host processor 1128 can also perform additional functionsthat may not be related to panel processing, and can be coupled toprogram storage 1132 and display device 1130 such as an LCD display forproviding a UI to a user of the device. In some embodiments, the hostprocessor 1128 can be a separate component from the touch controller1106, as shown. In other embodiments, the host processor 1128 can beincluded as part of the touch controller 1106. In still otherembodiments, the functions of the host processor 1128 can be performedby the processor subsystem 1102 and/or distributed among othercomponents of the touch controller 1106. The display device 1130together with the touch sensor panel 1124, when located partially orentirely under the touch sensor panel or when integrated with the touchsensor panel, can form a touch sensitive device such as a touch screen.

Note that one or more of the functions described above can be performed,for example, by firmware stored in memory (e.g., one of the peripherals)and executed by the processor subsystem 1102, or stored in the programstorage 1132 and executed by the host processor 1128. The firmware canalso be stored and/or transported within any non-transitory computerreadable storage medium for use by or in connection with an instructionexecution system, apparatus, or device, such as a computer-based system,processor-containing system, or other system that can fetch theinstructions from the instruction execution system, apparatus, or deviceand execute the instructions. In the context of this document, a“non-transitory computer readable storage medium” can be anynon-transitory medium that can contain or store the program for use byor in connection with the instruction execution system, apparatus, ordevice. The non-transitory computer readable storage medium can include,but is not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus or device,a portable computer diskette (magnetic), a random access memory (RAM)(magnetic), a read-only memory (ROM) (magnetic), an erasableprogrammable read-only memory (EPROM) (magnetic), a portable opticaldisc such a CD, CD-R, CD-RW, DVD, DVD-R, or DVD-RW, or flash memory suchas compact flash cards, secured digital cards, USB memory devices,memory sticks, and the like.

The firmware can also be propagated within any transport medium for useby or in connection with an instruction execution system, apparatus, ordevice, such as a computer-based system, processor-containing system, orother system that can fetch the instructions from the instructionexecution system, apparatus, or device and execute the instructions. Inthe context of this document, a “transport medium” can be anynon-transitory medium that can communicate, propagate or transport theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The transport medium can include, but isnot limited to, an electronic, magnetic, optical, electromagnetic orinfrared wired or wireless propagation medium.

It is to be understood that the touch panel is not limited to touch, asdescribed in FIG. 11, but can be a proximity panel or any other panelaccording to various embodiments. In addition, the touch sensor paneldescribed herein can be either a single-touch or a multi-touch sensorpanel.

It is further to be understood that the computing system is not limitedto the components and configuration of FIG. 11, but can include otherand/or additional components in various configurations capable ofcompensating for a negative pixel effect according to variousembodiments.

FIG. 12 illustrates an exemplary mobile telephone 1200 that can includetouch sensor panel 1224, display 1236, and other computing systemblocks, capable of reconstructing an original touch image from adifferential touch image according to various embodiments.

FIG. 13 illustrates an exemplary digital media player 1300 that caninclude touch sensor panel 1324, display 1336, and other computingsystem blocks, capable of reconstructing an original touch image from adifferential touch image according to various embodiments.

FIG. 14 illustrates an exemplary personal computer 1400 that can includetouch sensor panel (trackpad) 1424, display 1436, and other computingsystem blocks, capable of reconstructing an original touch image from adifferential touch image according to various embodiments.

The mobile telephone, media player, and personal computer of FIGS. 12through 14 can realize improved accuracy with capability to reconstructan original touch image from a differential touch image according tovarious embodiments.

Although embodiments have been fully described with reference to theaccompanying drawings, it is to be noted that various changes andmodifications will become apparent to those skilled in the art. Suchchanges and modifications are to be understood as being included withinthe scope of the various embodiments as defined by the appended claims.

What is claimed is:
 1. A method for reconstructing a first image from asecond image comprising: capturing the second image on a touch sensitivedevice; aligning columns of image data in the second image with eachother such that the columns have a common value, wherein an amount ofadjustment used to align each respective column of the columns is basedat least in part on data associated with the respective column and dataassociated with at least one other column; and aligning thecolumn-aligned image data with a baseline value to reconstruct the firstimage.
 2. The method of claim 1, wherein the reconstruction is based onthe image data alone.
 3. The method of claim 1, wherein: the amount ofadjustment used to align each respective column of the columns comprisesan offset for each column, and aligning columns of image data comprisescalculating the offset for each column, the offset indicative of theamount of misalignment of the column with the other columns.
 4. Themethod claim 1, wherein aligning the column-aligned image data comprisescalculating a global offset for the column-aligned image data, theoffset indicative of the amount of alignment to reach the baselinevalue.
 5. The method of claim 1, further comprising: capturing commonmode data associated with the second image, wherein the reconstructionis based on the image data and the common mode data.
 6. The method ofclaim 5, wherein: the amount of adjustment used to align each respectivecolumn of the columns comprises an offset for each column, and aligningcolumns of image data comprises: estimating a baseline value for eachcolumn based on the common mode data; and calculating the offset foreach column based on the estimated baseline value, the offset indicativeof the amount of alignment for the column with the other columns.
 7. Themethod of claim 5, wherein aligning the column-aligned image datacomprises: estimating a baseline value for each column based on thecommon mode data; and calculating a global offset for the column-alignedimage data based on the estimated baseline value, the offset indicativeof the amount of alignment to reach the baseline value.
 8. The method ofclaim 5, wherein: the amount of adjustment used to align each respectivecolumn of the columns comprises an offset for each column, and aligningcolumns of image data comprises: selectively utilizing the common modedata; and calculating the offset for each column based on theselectively utilized common mode data, the offset indicative of theamount of misalignment of the column with other columns.
 9. The methodof claim 5, wherein aligning the column-aligned image data comprises:selectively utilizing the common mode data; and calculating a globaloffset from the column-aligned image data based on the selectivelyutilized common mode data, the offset indicative of the amount ofalignment to reach the baseline value.
 10. The method of claim 1,further comprising: detecting an erroneous baseline value from the imagedata of the second image.
 11. A method for reconstructing an originaltouch image from a differential touch image comprising: capturing thedifferential touch image at a touch sensitive device; performing acolumn alignment to align columns of the differential touch image witheach other such that the columns have a common value, wherein an amountof adjustment used to align each respective column of the columns isbased at least in part on data associated with the respective column anddata associated with at least one other column; and performing abaseline alignment to align the column-aligned differential touch imagewith a baseline value, resulting in the reconstructed original touchimage.
 12. The method of claim 11, wherein capturing the differentialtouch image comprises performing a differential scan of the device toprovide image data in each column of the differential touch image havinga normalized sum of zero.
 13. The method of claim 11, wherein performinga column alignment comprises utilizing image data of the differentialtouch image corresponding to untouched regions of the device.
 14. Themethod of claim 11, wherein performing a baseline alignment comprisesutilizing image data of the differential touch image corresponding tountouched regions of the device.
 15. A method for reconstructing anoriginal touch image from a differential touch image, comprising:capturing the differential touch image at a touch sensitive device;capturing common mode data at the device; performing a column alignmentto align columns of the differential touch image to each other based onthe captured common mode data; and performing a baseline alignment toalign the column-aligned differential touch image with a baseline valuebased on the captured common mode data, resulting in the reconstructedoriginal touch image.
 16. The method of claim 15, comprising: estimatinga second baseline value for the differential touch image based on thecaptured common mode data; and utilizing the estimated second baselinevalue for the column and baseline alignments.
 17. The method of claim16, wherein estimating the second baseline value comprises: sortingimage data of the differential touch image having the captured commonmode data applied thereto among bins of a histogram; and identifying thebins having highest counts, the identified bins indicative of the secondbaseline value.
 18. A method for reconstructing an original touch imagefrom a differential touch image, comprising: capturing the differentialtouch image at a touch sensitive device; capturing common mode data atthe device; weighting the captured common mode data; performing a columnalignment to align columns of the differential touch image to each otherbased on the weighting; and performing a baseline alignment to align thecolumn-aligned differential touch image with a baseline value based onthe weighting, resulting in the reconstructed original touch image. 19.The method of claim 18, comprising: estimating a second baseline valuefor the differential touch image based on the captured common mode data;and weighting the second baseline value along with the captured commonmode data.
 20. The method of claim 19, wherein weighting the capturedcommon mode data comprises: weighting the captured common mode data moreheavily when non-touch image data in the differential touch image islimited, the non-touch data indicative of lack of a proximate object.21. A method for reconstructing an original touch image from adifferential touch image, comprising: capturing the differential touchimage at a touch sensitive device: calculating a parameter associatedwith at least some column data of the differential touch image; applyingthe parameter to the differential touch image to align columns of thedifferential touch image such that the columns have a common value; andreconstructing the original touch image.
 22. The method of claim 21,wherein the parameter is at least one of a minimum, a median, a mode, amean, or a weighted mean of the at least some column data.
 23. A touchsensitive device comprising: a touch panel capable of sensing aproximate object; scan logic couplable to the touch panel and capable ofperforming a scan of the touch panel so as to capture a first touchimage; and a processor capable of reconstructing a second touch imagefrom the first touch image, the reconstructing including: calculatingfirst alignments for portions of the first touch image, each portionhaving a different first alignment, aligning the portions with eachother according to the first alignments to form an aligned first imagesuch that the portions have a common value, wherein an amount ofadjustment used to align each respective portion of the portions isbased at least in part on data associated with the respective portionand data associated with at least one other portion, calculating asecond alignment for the aligned first image, and aligning the alignedfirst image according to the second alignment to meet a baseline valueso as to form the reconstructed second touch image.
 24. The device ofclaim 23, wherein the first image is a differential touch image and thesecond image is an original touch image.
 25. The device of claim 23comprising at least one of a computer, a media player, or a mobiletelephone.